One hot topic is always mMIMO performance and deployment implementation. It has to be tested thoroughly on the different beam directions, beam gains and beam management procedures. Emphasis should be given to beamforming and beam failure detection and recovery. Although those tests sound like a minor optimization steps, however @MCNS strongly believes that proper consideration would value up the expected traffic volume and investigate the handover and fallback scenarios requiring verification.
Another important aspect is on the way latency, jitter, packet loss, download, and upload speeds in a 5G environment are measured. In the past manual settings and testing configurations were of major interest in the operator’s networks. However nowadays a lot of different small tests have to be on board with the typical procedures, which makes the overall procedure quite difficult and impractical from a manual’s test configuration perspective.
The proposal is to use automated procedures and platforms with a lot of micro-settings and a large amount of different TCP/HTTP/UDP tests on different packet sizes and segmentation procedures. And that brings up the subject of Advanced Automated Testing Solutions which allows the thorough verification and comprehensive test analysis.
The other important aspect is the merging of Artificial Intelligence (AI) along with machine learning (ML) algorithms into the MAC scheduler and Radio Resource Management (RRM) procedures. All these AI/ML solutions are part of R&D implementation with non-disclosure on the market and the operators. And that poses several problems making planners/optimizers’ life difficult.
One brilliant idea is to use reverse engineering and try to “reversely” conclude on a mathematical model using linear and non-linear statistical analysis regression on drive test results. Having the models well-tuned, planners/optimizers could really improve network performance right on the vendor’s firmware implementations.
Operators should always revise their network deployment scenarios including also the NSA/SA network topology since it will determine the methodology used to verify the performance of the NR network deployment. Indeed several different deployment scenarios are possible for the rollout of NR networks and these deployment scenarios have an impact on network performance. The most common scenario is the overlay involving the deployment of NR equipment on the existing LTE sectors in a particular geographical area. The relative propagation characteristics of the NR channels should be studied in details as well as the coverage lag to LTE technology.
Finally the use of mmW bands might be the somehow problematic. For example in SA mmW deployments the accessibility and retainability performance of the NR network will not be influenced by other networks in the same way that the NSA architecture is. The throughput capabilities of the mmW SA deployment will be determined by the bandwidth of the NR carrier and the TDD frame structure allocations.
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